Rhythm generator using Variational Autoencoder (VAE). Based on M4L.RhythmVAE by Nao Tokui, modded and extended to support simple and compound meter rhythms, with minimal amount of training data. Similarly to RhythmVAE, the goal of R-VAE is the exploration of latent spaces of musical rhythms. Unlike most previous work in rhythm modeling, R-VAE can be trained with small datasets, enabling rapid customization and exploration by individual users. R-VAE employs a data representation that encodes simple and compound meter rhythms. Models and latent space visualizations for R-VAE are available on the project's GitHub page: https://github.com/vigliensoni/R-VAE-models.
Year: 2022
Website: https://github.com/vigliensoni/R-VAE
Input types: MIDI
Output types: MIDI
Output length: 2 bars
AI Technique: VAE
Dataset: "The Future Sample Pack"
License type: GPLv3
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Generates MIDI notes that are likely to follow the input drum beat or melody. Can extend the input of a specified MIDI clip by up to 32 measures. This can be helpful for adding variation to a drum beat or creating new material for a melodic track. It typically picks up on things like durations, key signatures and timing. It can be used to produce more random outputs by increasing the temperature. Ready to use as a Max for Live device. If you want to train the model on your own data or try different pre-trained models provided by the Magenta team, refer to the instructions on the team's GitHub page: https://github.com/magenta/magenta/tree/main/magenta/models/melody_rnn
Year: 2018
Website: https://magenta.tensorflow.org/studio#continue
Input types: MIDI
Output types: MIDI
Output length: 32 bars
AI Technique: LSTM
Dataset: MelodyRNN - Not disclosed; PerformanceRNN - The Piano- e-Competition dataset
License type: Apache 2.0
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Creates grooves based on the rhythm of any input. Can be used to generate a drum accompaniment to a bassline or melody, or to create a drum track from a tapped rhythm. Drumify works best with performed inputs, but it can also handle quantized clips. Ready to use as a Max for Live device. If you want to train the model on your own data or try different pre-trained models provided by the Magenta team, refer to the instructions on the team's GitHub page: https://github.com/magenta/magenta/tree/main/magenta/models/drums_rnn
Year: 2018
Website: https://magenta.tensorflow.org/studio#drumify
Input types: MIDI
Output types: MIDI
Output length: Input length
AI Technique: LSTM
Dataset: Expanded Groove MIDI dataset; Groove MIDI dataset
License type: Apache 2.0
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Music FaderNets is a controllable MIDI generation framework that models high-level musical qualities, such as emotional attributes like arousal. Drawing inspiration from the concept of sliding faders on a mixing console, the model offers intuitive and continuous control over these characteristics. Given an input MIDI, Music FaderNets can produce multiple variations with different levels of arousal, adjusted according to the position of the fader.
Year: 2020
Website: https://music-fadernets.github.io/
Input types: MIDI
Output types: MIDI
Output length:
AI Technique: VAE
Dataset: VGMIDI, Yamaha Piano-e-Competition
License type: MIT
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